Pinned Repositories
current-lane-drivable
本项目采用的网络模型为mask-rcnn，代码主要来源于开源项目[matterport project](https://github.com/matterport/Mask_RCNN)。
Docker_Tutorial
Lane-Detection-Based-PINet
the project use PINet as lane detector, supporting training on [VIL-100](https://github.com/yujun0-0/MMA-Net/tree/main/dataset). At the same time, the project supports model conversion, including onnx and caffe formats, as well as model forward acceleration processing before model deployment.Model forward acceleration mainly includes model cutting, simplification and merge batchnorm layer. Meanwhile, it includes the verification and comparison of the conformance after the model conversion.
Parse_Curvelanes
parse curvelanes datasets
parse_vil100
for parsing and converting dataset vil100.
SemanticSegmentation_DL
Resources of semantic segmantation based on Deep Learning model
state-of-the-art-result-for-machine-learning-problems
This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
toy-classification-pytorch
carrier of tricks for image classification tutorials using pytorch.
tutorials
机器学习相关教程
pandamax's Repositories
pandamax/tutorials
机器学习相关教程
pandamax/Anti-Anti-Spider
越来越多的网站具有反爬虫特性，有的用图片隐藏关键数据，有的使用反人类的验证码，建立反反爬虫的代码仓库，通过与不同特性的网站做斗争（无恶意）提高技术。（欢迎提交难以采集的网站）
pandamax/autoencoding_beyond_pixels
Generative image model with learned similarity measures
pandamax/awesome-autonomous-vehicles
Curated List of Self-Driving Cars and Autonomous Vehicles Resources
pandamax/CommAI-env
A platform for developing AI systems as described in A Roadmap towards Machine Intelligence - http://arxiv.org/abs/1511.08130
pandamax/CycleGAN
Software that generates photos from paintings, turns horses into zebras, performs style transfer, and more (from UC Berkeley)
pandamax/deep-residual-networks
Deep Residual Learning for Image Recognition
pandamax/FC-DenseNet
Fully Convolutional DenseNets for semantic segmentation.
pandamax/ganhacks
starter from "How to Train a GAN?" at NIPS2016
pandamax/generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
pandamax/High-Res-Neural-Inpainting
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
pandamax/Install-OpenCV
shell scripts to install different version of OpenCV in different distributions of Linux
pandamax/k-means-1
K-means
pandamax/keras-adversarial
Keras Generative Adversarial Networks
pandamax/LSGAN
Chainer implementation of Least Squares GAN (LSGAN)
pandamax/makeyourownneuralnetwork
Code for the Make Your Own Neural Network book
pandamax/mat-vae
A re-implementation of Auto-Encoding Variational Bayes in MATLAB
pandamax/matconvnet-calvin
Code for several state-of-the-art papers in object detection and semantic segmentation.
pandamax/openpilot
open source driving agent
pandamax/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
pandamax/scipy
Scipy library main repository
pandamax/SimGAN
Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training
pandamax/tensorflow
Computation using data flow graphs for scalable machine learning
pandamax/textvae
Theano code for experiments in the paper "A Hybrid Convolutional Variational Autoencoder for Text Generation."
pandamax/TF-Tutorials
A collection of deep learning tutorials using Tensorflow and Python
pandamax/VAE-TensorFlow
Implementation of a Variational Auto-Encoder in TensorFlow
pandamax/vaegan
An implementation of VAEGAN (variational autoencoder + generative adversarial network).
pandamax/WassersteinGAN.tensorflow
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
pandamax/wechat_callback
**特色深度学习训练插件，使用微信监控并控制keras训练过程
pandamax/weightnorm
Example code for Weight Normalization, from "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks"